void Trajectory2d::toGnuplot(const std::string& filename_without_suffix) const
{
const std::string gnuplot_filename = filename_without_suffix + ".gpl";
const std::string pose_filename = filename_without_suffix + ".gpldata";
const std::string curvature_filename = filename_without_suffix + "_curve.gpldata";
// -- data formatting --
std::ofstream pose_file(pose_filename.c_str());
pose_file << "# Trajectory data set containing "<< m_trajectory.size() << " sets of position and orientation information." << std::endl;
pose_file << "# To draw the poses the orientation is coded as two vectors in gnuplot from-delta style." << std::endl;
pose_file << "# from: x y | delta: x y" << std::endl;
pose_file << "# "<< std::endl;
pose_file << "# "<< std::endl;
pose_file << "# The trajectory is for "
<< (this->isForwardTrajectory() ? "FORWARD" : "BACKWARD")
<< " motion." << std::endl;
for (std::size_t i=0; i<m_trajectory.size(); ++i)
{
const Position2d from(m_trajectory[i].getPosition());
const Position2d delta = m_trajectory[i].getOrientation() * Position2d::UnitX() * 0.02;
pose_file << from.x() << " " << from.y() << " ";
if (velocityAvailable())
{
pose_file << m_trajectory[i].getVelocity() << " ";
}
pose_file << delta.x() << " " << delta.y() << " ";
if (velocityAvailable())
{
pose_file << 0.;
}
pose_file << std::endl;
}
pose_file << std::endl;
// curvature plots
if (curvatureAvailable())
{
std::ofstream curve_file(curvature_filename.c_str());
for (std::size_t i=0; i<m_trajectory.size(); ++i)
{
// start for curvature lines
const Position2d from(m_trajectory[i].getPosition());
// end point for curvature lines
const double curvature = m_trajectory[i].getCurvature() * 0.5; // apply scale for better visualization
const double yaw = m_trajectory[i].getYaw();
Position2d to;
to.x() = from.x() - std::sin(yaw) * curvature;
to.y() = from.y() + std::cos(yaw) * curvature;
curve_file << from.x() << " " << from.y();
if (velocityAvailable())
{
curve_file << " 0.";
}
curve_file << std::endl;
curve_file << to.x() << " " << to.y();
if (velocityAvailable())
{
curve_file << " 0.";
}
curve_file << std::endl << std::endl << std::endl; // apply empty line to separate plot
}
curve_file.close();
}
pose_file.close();
// -- the plot itself --
std::ofstream gnuplot_file(gnuplot_filename.c_str(), std::ios::out);
std::string plot_type = "plot";
velocityAvailable() ? plot_type = "splot" : plot_type = "plot";
gnuplot_file << "# Gnuplot file. Draw with gnuplot -p "<< pose_filename << std::endl;
gnuplot_file << "set mapping cartesian" << std::endl;
gnuplot_file << "set mouse" << std::endl;
gnuplot_file << "set size ratio -1" << std::endl;
gnuplot_file << plot_type + " '"<< pose_filename << "' using 1:2:3:4";
if (velocityAvailable())
{
gnuplot_file << ":5:6";
}
gnuplot_file << " with vector";
if (curvatureAvailable())
{
gnuplot_file << ", " << "'" << curvature_filename << "' using 1:2";
if (velocityAvailable())
{
gnuplot_file << ":3";
}
gnuplot_file << " w l";
}
gnuplot_file << std::endl;
gnuplot_file.close();
std::cout << "GnuPlot file written to " << gnuplot_filename << std::endl;
}
void pcl::face_detection::FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::initialize(std::string & data_dir)
{
std::string start;
std::string ext = std::string ("pcd");
bf::path dir = data_dir;
std::vector < std::string > files;
getFilesInDirectory (dir, start, files, ext);
//apart from loading the file names, we will do some bining regarding pitch and yaw
std::vector < std::vector<int> > yaw_pitch_bins;
std::vector < std::vector<std::vector<std::string> > > image_files_per_bin;
float res_yaw = 15.f;
float res_pitch = res_yaw;
int min_yaw = -75;
int min_pitch = -60;
int num_yaw = static_cast<int>((std::abs (min_yaw) * 2) / static_cast<int>(res_yaw + 1.f));
int num_pitch = static_cast<int>((std::abs (min_pitch) * 2) / static_cast<int>(res_pitch + 1.f));
yaw_pitch_bins.resize (num_yaw);
image_files_per_bin.resize (num_yaw);
for (int i = 0; i < num_yaw; i++)
{
yaw_pitch_bins[i].resize (num_pitch);
image_files_per_bin[i].resize (num_pitch);
for (int j = 0; j < num_pitch; j++)
{
yaw_pitch_bins[i][j] = 0;
}
}
for (size_t i = 0; i < files.size (); i++)
{
std::stringstream filestream;
filestream << data_dir << "/" << files[i];
std::string file = filestream.str ();
std::string pose_file (files[i]);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4f pose_mat;
pose_mat.setIdentity (4, 4);
std::stringstream filestream_pose;
filestream_pose << data_dir << "/" << pose_file;
pose_file = filestream_pose.str ();
bool result = readMatrixFromFile (pose_file, pose_mat);
if (result)
{
Eigen::Vector3f ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
ea *= 57.2957795f; //transform it to degrees to do the binning
int y = static_cast<int>(pcl_round ((ea[0] + static_cast<float>(std::abs (min_yaw))) / res_yaw));
int p = static_cast<int>(pcl_round ((ea[1] + static_cast<float>(std::abs (min_pitch))) / res_pitch));
if (y < 0)
y = 0;
if (p < 0)
p = 0;
if (p >= num_pitch)
p = num_pitch - 1;
if (y >= num_yaw)
y = num_yaw - 1;
assert (y >= 0 && y < num_yaw);
assert (p >= 0 && p < num_pitch);
yaw_pitch_bins[y][p]++;
image_files_per_bin[y][p].push_back (file);
}
}
pcl::face_detection::showBining (num_pitch, res_pitch, min_pitch, num_yaw, res_yaw, min_yaw, yaw_pitch_bins);
int max_elems = 0;
int total_elems = 0;
for (int i = 0; i < num_yaw; i++)
{
for (int j = 0; j < num_pitch; j++)
{
total_elems += yaw_pitch_bins[i][j];
if (yaw_pitch_bins[i][j] > max_elems)
max_elems = yaw_pitch_bins[i][j];
}
}
float average = static_cast<float> (total_elems) / (static_cast<float> (num_pitch + num_yaw));
std::cout << "The average number of image per bin is:" << average << std::endl;
std::cout << "Total number of images in the dataset:" << total_elems << std::endl;
//reduce unbalance from dataset by capping the number of images per bin, keeping at least a certain min
if (min_images_per_bin_ != -1)
{
std::cout << "Reducing unbalance of the dataset." << std::endl;
for (int i = 0; i < num_yaw; i++)
{
for (int j = 0; j < num_pitch; j++)
{
if (yaw_pitch_bins[i][j] >= min_images_per_bin_)
{
std::random_shuffle (image_files_per_bin[i][j].begin (), image_files_per_bin[i][j].end ());
image_files_per_bin[i][j].resize (min_images_per_bin_);
yaw_pitch_bins[i][j] = min_images_per_bin_;
}
for (size_t ii = 0; ii < image_files_per_bin[i][j].size (); ii++)
{
image_files_.push_back (image_files_per_bin[i][j][ii]);
}
}
}
}
pcl::face_detection::showBining (num_pitch, res_pitch, min_pitch, num_yaw, res_yaw, min_yaw, yaw_pitch_bins);
std::cout << "Total number of images in the dataset:" << image_files_.size () << std::endl;
}
void InitGui() {
InitTrackerGui(gui_vars, window_width, window_height, image_width,
image_height, rig.cameras_.size());
// std::cerr << "Viewport: " << gui_vars.camera_view->v.l << " " <<
// gui_vars.camera_view->v.r() << " " <<
// gui_vars.camera_view->v.b << " " <<
// gui_vars.camera_view->v.t() << std::endl;
pangolin::RegisterKeyPressCallback(
pangolin::PANGO_SPECIAL + pangolin::PANGO_KEY_RIGHT,
[&]() { is_stepping = true; });
pangolin::RegisterKeyPressCallback(pangolin::PANGO_CTRL + 'r', [&]() {
camera_img.reset();
is_keyframe = true;
is_prev_keyframe = true;
is_running = false;
InitTracker();
poses.clear();
gui_vars.scene_graph.Clear();
gui_vars.scene_graph.AddChild(&gui_vars.grid);
axes.clear();
LoadCameras();
prev_delta_t_ba = Sophus::SE3d();
prev_t_ba = Sophus::SE3d();
last_t_ba = Sophus::SE3d();
});
pangolin::RegisterKeyPressCallback(pangolin::PANGO_CTRL + 's', [&]() {
// write all the poses to a file.
std::ofstream pose_file("poses.txt", std::ios_base::trunc);
for (auto pose : poses) {
pose_file << pose->t_wp.translation().transpose().format(
sdtrack::kLongCsvFmt) << std::endl;
}
});
pangolin::RegisterKeyPressCallback(' ', [&]() { is_running = !is_running; });
pangolin::RegisterKeyPressCallback('b', [&]() {
// last_optimization_level = 0;
tracker.OptimizeTracks(last_optimization_level, optimize_landmarks,
optimize_pose);
UpdateCurrentPose();
});
pangolin::RegisterKeyPressCallback('B', [&]() {
do_bundle_adjustment = !do_bundle_adjustment;
std::cerr << "Do BA:" << do_bundle_adjustment << std::endl;
});
pangolin::RegisterKeyPressCallback('S', [&]() {
do_start_new_landmarks = !do_start_new_landmarks;
std::cerr << "Do SNL:" << do_start_new_landmarks << std::endl;
});
pangolin::RegisterKeyPressCallback('2',
[&]() { last_optimization_level = 2; });
pangolin::RegisterKeyPressCallback('3',
[&]() { last_optimization_level = 3; });
pangolin::RegisterKeyPressCallback('1',
[&]() { last_optimization_level = 1; });
pangolin::RegisterKeyPressCallback('0',
[&]() { last_optimization_level = 0; });
pangolin::RegisterKeyPressCallback('9', [&]() {
last_optimization_level = 0;
tracker.OptimizeTracks(-1, optimize_landmarks, optimize_pose);
UpdateCurrentPose();
});
pangolin::RegisterKeyPressCallback('p', [&]() {
tracker.PruneTracks();
// Update the pose t_ab based on the result from the tracker.
UpdateCurrentPose();
BaAndStartNewLandmarks();
});
pangolin::RegisterKeyPressCallback('l', [&]() {
optimize_landmarks = !optimize_landmarks;
std::cerr << "optimize landmarks: " << optimize_landmarks << std::endl;
});
pangolin::RegisterKeyPressCallback('c', [&]() {
optimize_pose = !optimize_pose;
std::cerr << "optimize pose: " << optimize_pose << std::endl;
});
pangolin::RegisterKeyPressCallback('m', [&]() {
is_manual_mode = !is_manual_mode;
std::cerr << "Manual mode:" << is_manual_mode << std::endl;
});
pangolin::RegisterKeyPressCallback('k', [&]() {
sdtrack::AlignmentOptions options;
options.apply_to_kp = true;
tracker.Do2dAlignment(options, tracker.GetImagePyramid(),
tracker.GetCurrentTracks(), last_optimization_level);
});
}
void pcl::face_detection::FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::getDatasetAndLabels(DataSet & data_set,
std::vector<LabelType> & label_data, std::vector<ExampleIndex> & examples)
{
srand (static_cast<unsigned int>(time (NULL)));
std::random_shuffle (image_files_.begin (), image_files_.end ());
std::vector < std::string > files;
files = image_files_;
files.resize (std::min (num_images_, static_cast<int> (files.size ())));
std::vector < TrainingExample > training_examples;
std::vector<float> labels;
int total_neg, total_pos;
total_neg = total_pos = 0;
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::visualization::PCLVisualizer vis("training");
#endif
for (size_t j = 0; j < files.size (); j++)
{
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
vis.removeAllPointClouds();
vis.removeAllShapes();
#endif
if ((j % 50) == 0)
{
std::cout << "Loading image..." << j << std::endl;
}
//1. Load clouds with and without labels
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr loaded_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile (files[j], *loaded_cloud);
pcl::PointCloud<pcl::PointXYZL>::Ptr cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::PointCloud<pcl::PointXYZL>::Ptr loaded_cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::io::loadPCDFile (files[j], *loaded_cloud_labels);
//crop images to remove as many NaNs as possible and reduce the memory footprint
{
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
for (size_t col = 0; col < loaded_cloud->width; col++)
{
for (size_t row = 0; row < loaded_cloud->height; row++)
{
if (pcl::isFinite (loaded_cloud->at (col, row)))
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
//std::cout << min_col << " - " << max_col << std::endl;
//std::cout << min_row << " - " << max_row << std::endl;
cropCloud<pcl::PointXYZ> (min_col, max_col, min_row, max_row, *loaded_cloud, *cloud);
cropCloud<pcl::PointXYZL> (min_col, max_col, min_row, max_row, *loaded_cloud_labels, *cloud_labels);
/*pcl::visualization::PCLVisualizer vis ("training");
vis.addPointCloud(loaded_cloud);
vis.spin();*/
}
//Compute integral image over depth
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_depth;
integral_image_depth.reset (new pcl::IntegralImage2D<float, 1> (false));
int element_stride = sizeof(pcl::PointXYZ) / sizeof(float);
int row_stride = element_stride * cloud->width;
const float *data = reinterpret_cast<const float*> (&cloud->points[0]);
integral_image_depth->setInput (data + 2, cloud->width, cloud->height, element_stride, row_stride);
//Compute normals and normal integral images
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
if (USE_NORMALS_)
{
typedef typename pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> NormalEstimator_;
NormalEstimator_ n3d;
n3d.setNormalEstimationMethod (n3d.COVARIANCE_MATRIX);
n3d.setInputCloud (cloud);
n3d.setRadiusSearch (0.02);
n3d.setKSearch (0);
{
pcl::ScopeTime t ("compute normals...");
n3d.compute (*normals);
}
}
int element_stride_normal = sizeof(pcl::Normal) / sizeof(float);
int row_stride_normal = element_stride_normal * normals->width;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_x;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_y;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_z;
if (USE_NORMALS_)
{
integral_image_normal_x.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nx = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_x->setInput (data_nx, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_y.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_ny = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_y->setInput (data_ny + 1, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_z.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nz = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_z->setInput (data_nz + 2, normals->width, normals->height, element_stride_normal, row_stride_normal);
}
//Using cloud labels estimate a 2D window from where to extract positive samples
//Rest can be used to extract negative samples
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
//std::cout << cloud_labels->width << " " << cloud_labels->height << std::endl;
for (size_t col = 0; col < cloud_labels->width; col++)
{
for (size_t row = 0; row < cloud_labels->height; row++)
{
if (cloud_labels->at (col, row).label == 1)
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity(new pcl::PointCloud<pcl::PointXYZI>);
cloud_intensity->width = cloud->width;
cloud_intensity->height = cloud->height;
cloud_intensity->points.resize(cloud->points.size());
cloud_intensity->is_dense = cloud->is_dense;
for (int jjj = 0; jjj < static_cast<int>(cloud->points.size()); jjj++)
{
cloud_intensity->points[jjj].getVector4fMap() = cloud->points[jjj].getVector4fMap();
cloud_intensity->points[jjj].intensity = 0.f;
int row, col;
col = jjj % cloud->width;
row = jjj / cloud->width;
//std::cout << row << " " << col << std::endl;
if (check_inside(col, row, min_col, max_col, min_row, max_row))
{
cloud_intensity->points[jjj].intensity = 1.f;
}
}
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity, "intensity");
vis.addPointCloud(cloud_intensity, handler, "intensity_cloud");
#endif
std::string pose_file (files[j]);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4f pose_mat;
pose_mat.setIdentity (4, 4);
readMatrixFromFile (pose_file, pose_mat);
Eigen::Vector3f ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
Eigen::Vector3f trans_vector = Eigen::Vector3f (pose_mat (0, 3), pose_mat (1, 3), pose_mat (2, 3));
pcl::PointXYZ center_point;
center_point.x = trans_vector[0];
center_point.y = trans_vector[1];
center_point.z = trans_vector[2];
int N_patches = patches_per_image_;
int pos_extracted = 0;
int neg_extracted = 0;
int w_size_2 = static_cast<int> (w_size_ / 2);
//************************************************
//2nd training style, fanelli's journal description
//************************************************
{
typedef std::pair<int, int> pixelpair;
std::vector < pixelpair > negative_p, positive_p;
//get negative and positive indices to sample from
for (int col = 0; col < (static_cast<int> (cloud_labels->width) - w_size_); col++)
{
for (int row = 0; row < (static_cast<int> (cloud_labels->height) - w_size_); row++)
{
if (!pcl::isFinite (cloud->at (col + w_size_2, row + w_size_2)))
continue;
//reject patches with more than percent invalid values
float percent = 0.5f;
if (static_cast<float>(integral_image_depth->getFiniteElementsCount (col, row, w_size_, w_size_)) < (percent * static_cast<float>(w_size_ * w_size_)))
continue;
pixelpair pp = std::make_pair (col, row);
if (cloud_labels->at (col + w_size_2, row + w_size_2).label == 1)
positive_p.push_back (pp);
else
negative_p.push_back (pp);
}
}
//shuffle and resize
std::random_shuffle (positive_p.begin (), positive_p.end ());
std::random_shuffle (negative_p.begin (), negative_p.end ());
positive_p.resize (N_patches);
negative_p.resize (N_patches);
//extract positive patch
for (size_t p = 0; p < positive_p.size (); p++)
{
int col, row;
col = positive_p[p].first;
row = positive_p[p].second;
pcl::PointXYZ patch_center_point;
patch_center_point.x = cloud->at (col + w_size_2, row + w_size_2).x;
patch_center_point.y = cloud->at (col + w_size_2, row + w_size_2).y;
patch_center_point.z = cloud->at (col + w_size_2, row + w_size_2).z;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
te.trans_ = center_point.getVector3fMap () - patch_center_point.getVector3fMap ();
te.trans_ *= 1000.f; //transform it to millimeters
te.rot_ = ea;
te.rot_ *= 57.2957795f; //transform it to degrees
labels.push_back (1);
pos_extracted++;
total_pos++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
std::cout << "Going to extract negative patches..." << std::endl;
sleep(2);
#endif
for (size_t p = 0; p < negative_p.size (); p++)
{
int col, row;
col = negative_p[p].first;
row = negative_p[p].second;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
labels.push_back (0);
neg_extracted++;
total_neg++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
if (neg_extracted != N_patches)
{
std::cout << "Extracted " << neg_extracted << " negative patches" << std::endl;
std::cout << files[j] << std::endl;
}
if (pos_extracted != N_patches)
{
std::cout << "Extracted " << pos_extracted << " positive patches" << std::endl;
std::cout << files[j] << std::endl;
}
}
}
std::cout << training_examples.size () << " " << labels.size () << " " << total_neg << " " << total_pos << std::endl;
//train random forest and make persistent
std::vector<int> example_indices;
for (size_t i = 0; i < labels.size (); i++)
example_indices.push_back (static_cast<int> (i));
label_data = labels;
data_set = training_examples;
examples = example_indices;
}
void run(pcl::RFFaceDetectorTrainer & fdrf, typename pcl::PointCloud<PointInT>::Ptr & scene_vis, pcl::visualization::PCLVisualizer & vis, bool heat_map,
bool show_votes, const std::string & filename)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr scene (new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud (*scene_vis, *scene);
fdrf.setInputCloud (scene);
if (heat_map)
{
pcl::PointCloud<pcl::PointXYZI>::Ptr intensity_cloud (new pcl::PointCloud<pcl::PointXYZI>);
fdrf.setFaceHeatMapCloud (intensity_cloud);
}
fdrf.detectFaces ();
typedef typename pcl::traits::fieldList<PointInT>::type FieldListM;
double rgb_m;
bool exists_m;
pcl::for_each_type < FieldListM > (pcl::CopyIfFieldExists<PointInT, double> (scene_vis->points[0], "rgb", exists_m, rgb_m));
std::cout << "Color exists:" << static_cast<int> (exists_m) << std::endl;
if (exists_m)
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr to_visualize (new pcl::PointCloud<pcl::PointXYZRGB>);
pcl::copyPointCloud (*scene_vis, *to_visualize);
pcl::visualization::PointCloudColorHandlerRGBField < pcl::PointXYZRGB > handler_keypoints (to_visualize);
vis.addPointCloud < pcl::PointXYZRGB > (to_visualize, handler_keypoints, "scene_cloud");
} else
{
vis.addPointCloud (scene_vis, "scene_cloud");
}
if (heat_map)
{
pcl::PointCloud<pcl::PointXYZI>::Ptr intensity_cloud (new pcl::PointCloud<pcl::PointXYZI>);
fdrf.getFaceHeatMap (intensity_cloud);
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler_keypoints (intensity_cloud, "intensity");
vis.addPointCloud < pcl::PointXYZI > (intensity_cloud, handler_keypoints, "heat_map");
}
if (show_votes)
{
//display votes_
/*pcl::PointCloud<pcl::PointXYZ>::Ptr votes_cloud(new pcl::PointCloud<pcl::PointXYZ>());
fdrf.getVotes(votes_cloud);
pcl::visualization::PointCloudColorHandlerCustom < pcl::PointXYZ > handler_votes(votes_cloud, 255, 0, 0);
vis.addPointCloud < pcl::PointXYZ > (votes_cloud, handler_votes, "votes_cloud");
vis.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 14, "votes_cloud");
vis.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 0.5, "votes_cloud");
vis.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 0.75, "votes_cloud");*/
pcl::PointCloud<pcl::PointXYZI>::Ptr votes_cloud (new pcl::PointCloud<pcl::PointXYZI> ());
fdrf.getVotes2 (votes_cloud);
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler_votes (votes_cloud, "intensity");
vis.addPointCloud < pcl::PointXYZI > (votes_cloud, handler_votes, "votes_cloud");
vis.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 14, "votes_cloud");
}
vis.addCoordinateSystem (0.1, "global");
std::vector<Eigen::VectorXd> heads;
fdrf.getDetectedFaces (heads);
face_detection_apps_utils::displayHeads (heads, vis);
if (SHOW_GT)
{
//check if there is ground truth data
std::string pose_file (filename);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4d pose_mat;
pose_mat.setIdentity (4, 4);
bool result = face_detection_apps_utils::readMatrixFromFile (pose_file, pose_mat);
if (result)
{
Eigen::Vector3d ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
Eigen::Vector3d trans_vector = Eigen::Vector3d (pose_mat (0, 3), pose_mat (1, 3), pose_mat (2, 3));
std::cout << ea << std::endl;
std::cout << trans_vector << std::endl;
pcl::PointXYZ center_point;
center_point.x = trans_vector[0];
center_point.y = trans_vector[1];
center_point.z = trans_vector[2];
vis.addSphere (center_point, 0.05, 255, 0, 0, "sphere");
pcl::ModelCoefficients cylinder_coeff;
cylinder_coeff.values.resize (7); // We need 7 values
cylinder_coeff.values[0] = center_point.x;
cylinder_coeff.values[1] = center_point.y;
cylinder_coeff.values[2] = center_point.z;
cylinder_coeff.values[3] = ea[0];
cylinder_coeff.values[4] = ea[1];
cylinder_coeff.values[5] = ea[2];
Eigen::Vector3d vec = Eigen::Vector3d::UnitZ () * -1.;
Eigen::Matrix3d matrixxx;
matrixxx = Eigen::AngleAxisd (ea[0], Eigen::Vector3d::UnitX ()) * Eigen::AngleAxisd (ea[1], Eigen::Vector3d::UnitY ())
* Eigen::AngleAxisd (ea[2], Eigen::Vector3d::UnitZ ());
//matrixxx = pose_mat.block<3,3>(0,0);
vec = matrixxx * vec;
cylinder_coeff.values[3] = vec[0];
cylinder_coeff.values[4] = vec[1];
cylinder_coeff.values[5] = vec[2];
cylinder_coeff.values[6] = 0.01;
vis.addCylinder (cylinder_coeff, "cylinder");
}
}
vis.setRepresentationToSurfaceForAllActors ();
if (VIDEO)
{
vis.spinOnce (50, true);
} else
{
vis.spin ();
}
vis.removeAllPointClouds ();
vis.removeAllShapes ();
}
